code: def filterbank_mel(
      sample_rate,
      n_freq,
      n_mels=128,
      f_min=0.0,
      f_max=None,
      htk=False,
      norm="slaney",
  ):
      """A wrapper for librosa.filters.mel that additionally does transpose and tensor conversion
  
      Args:
          sample_rate (`int`): sample rate of the input audio
          n_freq (`int`): number of frequency bins in the input STFT magnitude.
          n_mels (`int`): the number of mel bands
          f_min (`float`): lowest frequency that is going to be included in the mel filterbank (Hertz)
          f_max (`float`): highest frequency that is going to be included in the mel filterbank (Hertz)
          htk (bool): whether to use `htk` formula or not
          norm: The default, 'slaney', would normalize the the mel weights by the width of the mel band.
  
      Returns:
          (`Tensor`): mel filterbanks. Shape=`(n_freq, n_mels)`
      """
      filterbank = librosa.filters.mel(
          sr=sample_rate,
          n_fft=(n_freq - 1) * 2,
          n_mels=n_mels,
          fmin=f_min,
          fmax=f_max,
          htk=htk,
          norm=norm,
      ).astype(K.floatx())
      return tf.convert_to_tensor(filterbank.T)
  
file_path: kapre/kapre/backend.py
imports: from tensorflow.keras import backend
  import tensorflow
  import numpy
  import librosa
label_desc: Use image node_id 4 for calling a global function with example usage: filterbank_mel(sample_rate, n_freq, n_mels, f_min, f_max, htk, norm) and returns: tf